Abstract: A system and method for classifying compartments at a security checkpoint includes classifying a compartment into a first category or a second category using a first stage neural network that analyzes a three-dimensional representation of the compartment extracted from an imaging device coupled to the computing system, and in response to classifying the compartment into the second category, screening the compartment using a second stage neural network that performs image segmentation to isolate a hazardous object present in the compartment.

Abstract: A system and method for automatically detecting prohibited materials in a compartment at a security checkpoint includes receiving a three-dimensional representation of a compartment from an imaging device connected to the computing system, and classifying each voxel of the three-dimensional representation using a trained neural network to determine whether any voxel classifications of the three-dimensional representation correspond to a voxel classification of a prohibited material.

Abstract: A system and method for automatically detecting prohibited objects in a compartment at a security checkpoint includes receiving a three-dimensional representation of a compartment from an imaging device connected to the computing system, identifying, by the processor, a region within the compartment that has a highest likelihood of containing a prohibited object, based on a voxel classification of the three-dimensional representation using a first trained neural network, and classifying, by the processor, shapes of objects located within the identified region using a second trained neural network to determine whether any of the shapes correspond to a shape of a prohibited object known by the second neural network.

Abstract: A system and method for automatically classifying compartments at a security checkpoint includes receiving a three-dimensional representation of a compartment from an imaging device connected to the computing system, and classifying the compartment into a first category or a second category using a trained neural network that analyzes the three-dimensional representation, wherein compartments classified into the first category are automatically cleared at the security checkpoint and compartments classified into the second category undergo further security checks.

Abstract: A system and method for automatically detecting prohibited materials in a compartment at a security checkpoint includes receiving a three-dimensional representation of a compartment from an imaging device connected to the computing system, and classifying each voxel of the three-dimensional representation using a trained neural network to determine whether any voxel classifications of the three-dimensional representation correspond to a voxel classification of a prohibited material.

Abstract: A system and method for automatically detecting prohibited objects in a compartment at a security checkpoint includes receiving a three-dimensional representation of a compartment from an imaging device connected to the computing system, identifying, by the processor, a region within the compartment that has a highest likelihood of containing a prohibited object, based on a voxel classification of the three-dimensional representation using a first trained neural network, and classifying, by the processor, shapes of objects located within the identified region using a second trained neural network to determine whether any of the shapes correspond to a shape of a prohibited object known by the second neural network.

Abstract: A system and method for automatically classifying compartments at a security checkpoint includes receiving a three-dimensional representation of a compartment from an imaging device connected to the computing system, and classifying the compartment into a first category or a second category using a trained neural network that analyzes the three-dimensional representation, wherein compartments classified into the first category are automatically cleared at the security checkpoint and compartments classified into the second category undergo further security checks.

Abstract: A method for controlling a peripheral device includes receiving input from a user at a workstation adapted to the user, determining whether the received input can be valid, generating a job ticket from the valid input, sending the job ticket to the peripheral device and receiving an identifier representing the job ticket from the peripheral device.

Abstract: A method for an impaired user to control a peripheral device including receiving key-value pair input from the user, determining whether the received input is valid and executing a job generated from the valid received key-value pair input.

Abstract: A system, method, and process for embedding low frequency image details and high frequency image details into a final digital image file. A digital image file is stored with embedded low and high frequency image details that are accessible with a compatible digital image decoder and a non-compatible digital image decoder. Non-compatible digital image decoders access only a compressed low frequency image of the original digital image contained in the final digital image file. Compatible digital image decoders use the embedded high frequency image details contained in the final digital image file to render a high resolution image.

Abstract: Pixel characteristics, such as whether a pixel forms part of the background of an image, are evaluated by computing histograms from pixel color space components, by comparing pixel color space components to threshold values, or by other methods.

Abstract: In an array of pixels, a method for segmenting a selected pixel of the array between at least two layers including identifying an N-by-N window centered upon the selected pixel, evaluating at least one pixel in the N-by-N window to determine whether the selected pixel is a potential text element, identifying an M-by-M window centered upon the selected pixel when the evaluation determines that the selected pixel is a potential text element, wherein the M-by-M window is smaller than the N-by-N window, and determining whether the potential text element includes text by comparing at least two pixels within the M-by-M window.

Abstract: A system, method, and process for embedding low frequency image details and high frequency image details into a final digital image file. A digital image file is stored with embedded low and high frequency image details that are accessible with a compatible digital image decoder and a non-compatible digital image decoder. Non-compatible digital image decoders access only a compressed low frequency image of the original digital image contained in the final digital image file. Compatible digital image decoders use the embedded high frequency image details contained in the final digital image file to render a high resolution image.

Abstract: A method, and a system employing the method, of enhancing an intensity of an image of a multi-page or multi-portion document is disclosed. The method includes determining a first background parameter from a first section of a scanned page and stretching data corresponding to the scanned page based on the first background parameter. The method also includes determining a second background parameter from data of a second section of the scanned page and stretching data corresponding to another scanned page based on the second background parameter.

Abstract: A method for JPEG encoding an image that includes preprocessing steps performed prior to a JPEG compression step. The preprocessing steps include transforming block image data, using a variable quantization table selected for each block according to the classification of pixels in that block to produce quantized image data, and inverse transforming the quantized image data to produce filtered image data.

Abstract: A method, computer program product, and scanning system for scanning a physical document to generate an original image-based file. The original image-based file is processed to extract text from the original image-based file and generate a text-based file. The text-based file is processed to identify one or more hyperlinks included within the text-based file. The original image-based file is modified to include the one or more hyperlinks identified within the text-based file, thus generating a modified image-based file.

Abstract: A scanner calibration strip medium is described which includes at least one of a plurality of text symbols and a plurality of halftoned non-text images. A scanner is described which includes a scan bar and a scanner calibration strip medium scannable by the scan bar. The calibration strip medium includes a plurality of text symbols having text pixels and a plurality of halftoned non-text images having halftoned non-text pixels. A method for segmenting a scanned document image into text symbols and halftoned non-text images is described which uses a scanner calibration strip medium which includes a plurality of text symbols having text pixels and a plurality of halftoned non-text symbols having halftoned non-text pixels, wherein the calibration strip medium has a known pixel classification including which pixels of the calibration strip medium are text pixels and which pixels of the calibration strip medium are halftoned non-text pixels.

Abstract: Pixel characteristics, such as whether a pixel forms part of the background of an image, are evaluated by computing histograms from pixel color space components, by comparing pixel color space components to threshold values, or by other methods.

Abstract: A method for copying objects smaller than a background defined by a scanner of an apparatus includes scanning a plurality of objects to acquire image data associated with the plurality of objects and the background; cropping the image data to remove object data for each object of the plurality of objects from the background; determining a size associated with each object of the plurality of objects, based on the object data; and formatting a page to produce a compact arrangement of multiple objects of the plurality of objects on the page, the page receiving the multiple objects in accordance with the size.

Abstract: Pixel characteristics, such as whether a pixel forms part of the background of an image, are evaluated by computing histograms from pixel color space components, by comparing pixel color space components to threshold values, or by other methods.